Does Data Augmentation Lead to Positive Margin? - Shashank Rajput; Zhili Feng; Zachary Charles; Po-Ling Loh; Dimitris S. Papailiopoulos (ICML, 2019)

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. - Shashank Rajput; Hongyi Wang 001; Zachary Charles; Dimitris S. Papailiopoulos (NeurIPS, 2019)

Convergence and Margin of Adversarial Training on Separable Data. - Zachary Charles; Shashank Rajput; Stephen J. Wright 001; Dimitris S. Papailiopoulos (CoRR, 2019)

Closing the convergence gap of SGD without replacement. - Shashank Rajput; Anant Gupta; Dimitris S. Papailiopoulos (ICML, 2020)

Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient. - Ankit Pensia; Shashank Rajput; Alliot Nagle; Harit Vishwakarma; Dimitris S. Papailiopoulos (NeurIPS, 2020)

Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. - Hongyi Wang 001; Kartik Sreenivasan; Shashank Rajput; Harit Vishwakarma; Saurabh Agarwal; Jy-yong Sohn; Kangwook Lee 001; Dimitris S. Papailiopoulos (NeurIPS, 2020)

Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient. - Ankit Pensia; Shashank Rajput; Alliot Nagle; Harit Vishwakarma; Dimitris S. Papailiopoulos (CoRR, 2020)

An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks. - Shashank Rajput; Kartik Sreenivasan; Dimitris S. Papailiopoulos; Amin Karbasi (NeurIPS, 2021)

Finding Everything within Random Binary Networks. - Kartik Sreenivasan; Shashank Rajput; Jy-yong Sohn; Dimitris S. Papailiopoulos (CoRR, 2021)

Finding Nearly Everything within Random Binary Networks. - Kartik Sreenivasan; Shashank Rajput; Jy-yong Sohn; Dimitris S. Papailiopoulos (AISTATS, 2022)

Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment. - Tuan Dinh; Jy-yong Sohn; Shashank Rajput; Timothy Ossowski; Yifei Ming; Junjie Hu 001; Dimitris S. Papailiopoulos; Kangwook Lee 001 (EMNLP (Findings), 2022)

Permutation-Based SGD: Is Random Optimal? - Shashank Rajput; Kangwook Lee 001; Dimitris S. Papailiopoulos (ICLR, 2022)

Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. - Chulhee Yun; Shashank Rajput; Suvrit Sra (ICLR, 2022)

LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks. - Tuan Dinh; Yuchen Zeng; Ruisu Zhang; Ziqian Lin; Michael Gira; Shashank Rajput; Jy-yong Sohn; Dimitris S. Papailiopoulos; Kangwook Lee 001 (NeurIPS, 2022)

The Expressive Power of Tuning Only the Normalization Layers. - Angeliki Giannou; Shashank Rajput; Dimitris Papailiopoulos (COLT, 2023)

Looped Transformers as Programmable Computers. - Angeliki Giannou; Shashank Rajput; Jy-yong Sohn; Kangwook Lee 001; Jason D. Lee; Dimitris Papailiopoulos (ICML, 2023)

Recommender Systems with Generative Retrieval. - Shashank Rajput; Nikhil Mehta 002; Anima Singh; Raghunandan Hulikal Keshavan; Trung Vu 002; Lukasz Heldt; Lichan Hong; Yi Tay; Vinh Q. Tran 002; Jonah Samost; Maciej Kula; Ed H. Chi; et al. (NeurIPS, 2023)

The Expressive Power of Tuning Only the Norm Layers. - Angeliki Giannou; Shashank Rajput; Dimitris S. Papailiopoulos (CoRR, 2023)

Inference-Friendly Models With MixAttention. - Shashank Rajput; Ying Sheng 007; Sean Owen; Vitaliy Chiley (ENLSP, 2024)

Maestro: Uncovering Low-Rank Structures via Trainable Decomposition. - Samuel Horváth; Stefanos Laskaridis; Shashank Rajput; Hongyi Wang 001 (ICML, 2024)